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Record W2790554227 · doi:10.1177/1559827617751684

Nutritional Considerations for Sport Participation in Children and Adolescents With Obesity

2018· review· en· W2790554227 on OpenAlexaff
Lisa Chu, Brian W. Timmons

Bibliographic record

VenueAmerican Journal of Lifestyle Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineObesityChildhood obesityPhysical activityPhysical therapyPediatricsEnvironmental healthGerontologyOverweightEndocrinology

Abstract

fetched live from OpenAlex

There is a high prevalence of children with obesity who are participating in sports. Appropriate nutritional considerations are important to optimize health and training adaptations. This review focuses on macronutrient recommendations and their effect on weight management and/or benefits for athletic training for children ages 5 to 18 years. Equal distribution of protein intake throughout the day (~25-30 g/meal) and during postexercise recovery is recommended. Special attention should be given to increasing protein intake during breakfast because it is often the meal with the least protein intake. Both postexercise recommendations for protein (~0.3 g/kg of body weight) and carbohydrate (~1.0-1.2 g/kg/h) were not determined in children with obesity, and require future verification. Individual carbohydrate needs of training to meet fuel costs are recommended, but ~200 to 500 g/day of carbohydrate may be required depending on a child's level of sport participation and competition. Fat intake should follow general recommendations to meet the accepted macronutrient distribution range in children (25% to 35%) and reduce saturated fat intake. No evidence suggests that additional dietary fat modifications would improve training adaptations in children. Longitudinal studies are required to further our understanding of age and sex effects and confirm the appropriate quantity of macronutrients for active children with obesity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.343
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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